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Deep Neural Networks for Dental Implant System Classification.
Shintaro Sukegawa1,2, Kazumasa Yoshii3, Takeshi Hara3
1Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan.
Biomolecules
|July 8, 2020
Summary
Deep convolutional neural networks (CNNs) accurately classify dental implant brands from panoramic X-rays. Finely tuned VGG16 and VGG19 models demonstrated superior performance in this dental implant classification task.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Imaging Analysis
- Dental Implantology
Background:
- Accurate identification of dental implant brands is crucial for treatment planning and patient care.
- Traditional methods for implant identification can be time-consuming and subjective.
- Advancements in deep learning offer potential for automated and objective analysis of dental radiographs.
Purpose of the Study:
- To evaluate the accuracy of deep convolutional neural networks (CNNs) in classifying different dental implant brands using panoramic X-ray images.
- To compare the performance of various CNN models, including transfer-learning strategies, for dental implant classification.
- To determine the optimal CNN model for reliable identification of 11 distinct dental implant systems.
Main Methods:
- Utilized a dataset of 8859 panoramic X-ray images from 11 dental implant systems.
- Trained and evaluated five deep CNN models: a basic CNN, VGG16, VGG19 (transfer learning), and finely tuned VGG16, VGG19.
- Employed transfer-learning strategies to enhance model performance for implant classification.
- Assessed classification accuracy based on objective labeling from digital panoramic radiographs.
Main Results:
- The finely tuned VGG16 model achieved the highest accuracy in classifying dental implant brands.
- The finely tuned VGG19 model demonstrated the second-best performance.
- Standard transfer-learning VGG16 also showed promising results, outperforming the basic CNN.
- CNN models effectively classified implant systems from panoramic X-ray images.
Conclusions:
- Deep convolutional neural networks, particularly finely tuned VGG16 and VGG19, can accurately classify dental implant systems from panoramic X-rays.
- Transfer learning significantly improves the performance of CNNs for dental implant brand identification.
- This AI-driven approach offers a reliable and objective method for dental implant classification in clinical settings.
Keywords:
artificial intelligenceclassificationconvolutional neural networksdeep learningdental implant
